Overview

Dataset statistics

Number of variables10
Number of observations4716
Missing cells8807
Missing cells (%)18.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory368.6 KiB
Average record size in memory80.0 B

Variable types

Numeric8
Categorical2

Alerts

Date has a high cardinality: 86 distinct valuesHigh cardinality
Experimental_Year is highly overall correlated with DateHigh correlation
Dry_Matter_Prop is highly overall correlated with Date and 1 other fieldsHigh correlation
Starch is highly overall correlated with CropHigh correlation
Date is highly overall correlated with Experimental_Year and 2 other fieldsHigh correlation
Crop is highly overall correlated with Dry_Matter_Prop and 2 other fieldsHigh correlation
Thousand_Kernel_Mass has 94 (2.0%) missing valuesMissing
Crude_Protein has 1071 (22.7%) missing valuesMissing
Starch has 3817 (80.9%) missing valuesMissing
Crude_Oil has 3808 (80.7%) missing valuesMissing
Dry_Matter_Prop is highly skewed (γ1 = 1.47291243)Skewed
Thousand_Kernel_Mass is highly skewed (γ1 = 1.524612326)Skewed

Reproduction

Analysis started2024-07-19 13:44:31.786812
Analysis finished2024-07-19 13:44:44.533958
Duration12.75 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Experimental_Year
Real number (ℝ)

Distinct20
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Skewness-0.0327
Mean2.01 × 103
Minimum2 × 103
Maximum2.02 × 103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.0 KiB
2024-07-19T15:44:44.603418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2 × 103
5-th percentile2 × 103
Q12.01 × 103
median2.01 × 103
Q32.02 × 103
95-th percentile2.02 × 103
Maximum2.02 × 103
Range19
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.78
Coefficient of variation (CV)0.00287
Kurtosis-1.2
Mean2.01 × 103
Median Absolute Deviation (MAD)5
Skewness-0.0327
Sum9.5 × 106
Variance33.4
MonotonicityIncreasing
2024-07-19T15:44:44.740554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2004 240
 
5.1%
2005 240
 
5.1%
2022 240
 
5.1%
2021 240
 
5.1%
2020 240
 
5.1%
2019 240
 
5.1%
2018 240
 
5.1%
2017 240
 
5.1%
2016 240
 
5.1%
2015 240
 
5.1%
Other values (10) 2316
49.1%
ValueCountFrequency (%)
2004 240
5.1%
2005 240
5.1%
2006 240
5.1%
2007 228
4.8%
2008 216
4.6%
ValueCountFrequency (%)
2023 240
5.1%
2022 240
5.1%
2021 240
5.1%
2020 240
5.1%
2019 240
5.1%

Date
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct86
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size37.0 KiB
2004-08-09 00:00:00.0000000
 
96
2014-08-01 00:00:00.0000000
 
96
2010-08-20 00:00:00.0000000
 
96
2019-07-23 00:00:00.0000000
 
96
2012-08-01 00:00:00.0000000
 
96
Other values (81)
4236 

Length

Max length27
Median length27
Mean length27
Min length27

Characters and Unicode

Total characters127332
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2004-08-09 00:00:00.0000000
2nd row2004-08-09 00:00:00.0000000
3rd row2004-08-09 00:00:00.0000000
4th row2004-08-09 00:00:00.0000000
5th row2004-08-09 00:00:00.0000000

Common Values

ValueCountFrequency (%)
2004-08-09 00:00:00.0000000 96
 
2.0%
2014-08-01 00:00:00.0000000 96
 
2.0%
2010-08-20 00:00:00.0000000 96
 
2.0%
2019-07-23 00:00:00.0000000 96
 
2.0%
2012-08-01 00:00:00.0000000 96
 
2.0%
2009-08-03 00:00:00.0000000 96
 
2.0%
2008-08-01 00:00:00.0000000 96
 
2.0%
1899-12-31 00:00:00.0000000 96
 
2.0%
2011-07-28 00:00:00.0000000 96
 
2.0%
2013-08-12 00:00:00.0000000 96
 
2.0%
Other values (76) 3756
79.6%

Length

2024-07-19T15:44:44.899256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00.0000000 4716
50.0%
2004-08-09 96
 
1.0%
2011-07-28 96
 
1.0%
2005-07-25 96
 
1.0%
2006-07-26 96
 
1.0%
2017-07-31 96
 
1.0%
2007-07-20 96
 
1.0%
2013-08-12 96
 
1.0%
2015-07-30 96
 
1.0%
1899-12-31 96
 
1.0%
Other values (77) 3852
40.8%

Most occurring characters

ValueCountFrequency (%)
0 74352
58.4%
- 9432
 
7.4%
: 9432
 
7.4%
2 8304
 
6.5%
1 5688
 
4.5%
4716
 
3.7%
. 4716
 
3.7%
7 3828
 
3.0%
3 1584
 
1.2%
8 1488
 
1.2%
Other values (4) 3792
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99036
77.8%
Other Punctuation 14148
 
11.1%
Dash Punctuation 9432
 
7.4%
Space Separator 4716
 
3.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 74352
75.1%
2 8304
 
8.4%
1 5688
 
5.7%
7 3828
 
3.9%
3 1584
 
1.6%
8 1488
 
1.5%
9 1320
 
1.3%
6 1032
 
1.0%
5 768
 
0.8%
4 672
 
0.7%
Other Punctuation
ValueCountFrequency (%)
: 9432
66.7%
. 4716
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 9432
100.0%
Space Separator
ValueCountFrequency (%)
4716
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 127332
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 74352
58.4%
- 9432
 
7.4%
: 9432
 
7.4%
2 8304
 
6.5%
1 5688
 
4.5%
4716
 
3.7%
. 4716
 
3.7%
7 3828
 
3.0%
3 1584
 
1.2%
8 1488
 
1.2%
Other values (4) 3792
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 127332
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 74352
58.4%
- 9432
 
7.4%
: 9432
 
7.4%
2 8304
 
6.5%
1 5688
 
4.5%
4716
 
3.7%
. 4716
 
3.7%
7 3828
 
3.0%
3 1584
 
1.2%
8 1488
 
1.2%
Other values (4) 3792
 
3.0%

Plot_ID
Real number (ℝ)

Distinct240
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Skewness0.00027
Mean120
Minimum1
Maximum240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.0 KiB
2024-07-19T15:44:45.047698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q161
median120
Q3180
95-th percentile229
Maximum240
Range239
Interquartile range (IQR)119

Descriptive statistics

Standard deviation69.3
Coefficient of variation (CV)0.576
Kurtosis-1.2
Mean120
Median Absolute Deviation (MAD)60
Skewness0.00027
Sum5.68 × 105
Variance4.81 × 103
MonotonicityNot monotonic
2024-07-19T15:44:45.194006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 20
 
0.4%
98 20
 
0.4%
147 20
 
0.4%
148 20
 
0.4%
149 20
 
0.4%
150 20
 
0.4%
151 20
 
0.4%
152 20
 
0.4%
153 20
 
0.4%
154 20
 
0.4%
Other values (230) 4516
95.8%
ValueCountFrequency (%)
1 20
0.4%
2 20
0.4%
3 20
0.4%
4 20
0.4%
5 20
0.4%
ValueCountFrequency (%)
240 20
0.4%
239 20
0.4%
238 20
0.4%
237 20
0.4%
236 20
0.4%

Crop
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size37.0 KiB
Winter wheat
1920 
Grain maize
948 
Winter rapeseed
936 
Winter barley
888 
Summer rapeseed
 
24

Length

Max length15
Median length13
Mean length12.6
Min length11

Characters and Unicode

Total characters59412
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWinter wheat
2nd rowWinter wheat
3rd rowWinter wheat
4th rowWinter wheat
5th rowWinter wheat

Common Values

ValueCountFrequency (%)
Winter wheat 1920
40.7%
Grain maize 948
20.1%
Winter rapeseed 936
19.8%
Winter barley 888
18.8%
Summer rapeseed 24
 
0.5%

Length

2024-07-19T15:44:45.366687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-19T15:44:45.548997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
winter 3744
39.7%
wheat 1920
20.4%
rapeseed 960
 
10.2%
grain 948
 
10.1%
maize 948
 
10.1%
barley 888
 
9.4%
summer 24
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 10404
17.5%
r 6564
11.0%
t 5664
9.5%
a 5664
9.5%
i 5640
9.5%
4716
7.9%
n 4692
7.9%
W 3744
 
6.3%
w 1920
 
3.2%
h 1920
 
3.2%
Other values (11) 8484
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 49980
84.1%
Space Separator 4716
 
7.9%
Uppercase Letter 4716
 
7.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10404
20.8%
r 6564
13.1%
t 5664
11.3%
a 5664
11.3%
i 5640
11.3%
n 4692
9.4%
w 1920
 
3.8%
h 1920
 
3.8%
m 996
 
2.0%
s 960
 
1.9%
Other values (7) 5556
11.1%
Uppercase Letter
ValueCountFrequency (%)
W 3744
79.4%
G 948
 
20.1%
S 24
 
0.5%
Space Separator
ValueCountFrequency (%)
4716
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 54696
92.1%
Common 4716
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10404
19.0%
r 6564
12.0%
t 5664
10.4%
a 5664
10.4%
i 5640
10.3%
n 4692
8.6%
W 3744
 
6.8%
w 1920
 
3.5%
h 1920
 
3.5%
m 996
 
1.8%
Other values (10) 7488
13.7%
Common
ValueCountFrequency (%)
4716
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 10404
17.5%
r 6564
11.0%
t 5664
9.5%
a 5664
9.5%
i 5640
9.5%
4716
7.9%
n 4692
7.9%
W 3744
 
6.3%
w 1920
 
3.2%
h 1920
 
3.2%
Other values (11) 8484
14.3%

Yield_Total
Real number (ℝ)

Distinct3519
Distinct (%)74.9%
Missing17
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Skewness-0.0168
Mean70.7
Minimum2.96
Maximum201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.0 KiB
2024-07-19T15:44:45.710742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.96
5-th percentile29
Q152.2
median71.6
Q389.3
95-th percentile111
Maximum201
Range198
Interquartile range (IQR)37.1

Descriptive statistics

Standard deviation25.7
Coefficient of variation (CV)0.364
Kurtosis-0.48
Mean70.7
Median Absolute Deviation (MAD)18.4
Skewness-0.0168
Sum3.32 × 105
Variance662
MonotonicityNot monotonic
2024-07-19T15:44:45.865613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
73.2 9
 
0.2%
74.2 9
 
0.2%
74.5 8
 
0.2%
62.9 7
 
0.1%
68.1 7
 
0.1%
73.8 7
 
0.1%
65.2 7
 
0.1%
91.2 7
 
0.1%
87.9 7
 
0.1%
75.5 7
 
0.1%
Other values (3509) 4624
98.0%
(Missing) 17
 
0.4%
ValueCountFrequency (%)
2.955555556 1
< 0.1%
3.751282051 1
< 0.1%
5 1
< 0.1%
5.4 1
< 0.1%
6.479487179 1
< 0.1%
ValueCountFrequency (%)
201.2907199 1
< 0.1%
159.3853672 1
< 0.1%
153.3623031 1
< 0.1%
140.9834025 1
< 0.1%
138.3637209 1
< 0.1%

Dry_Matter_Prop
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Skewness1.47
Mean87
Minimum86
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.0 KiB
2024-07-19T15:44:46.019975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum86
5-th percentile86
Q186
median86
Q386
95-th percentile91
Maximum91
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.01
Coefficient of variation (CV)0.0231
Kurtosis0.17
Mean87
Median Absolute Deviation (MAD)0
Skewness1.47
Sum4.1 × 105
Variance4.05
MonotonicityNot monotonic
2024-07-19T15:44:46.171098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
86 3756
79.6%
91 960
 
20.4%
ValueCountFrequency (%)
86 3756
79.6%
91 960
 
20.4%
ValueCountFrequency (%)
91 960
 
20.4%
86 3756
79.6%

Thousand_Kernel_Mass
Real number (ℝ)

MISSING  SKEWED 

Distinct2559
Distinct (%)55.4%
Missing94
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Skewness1.52
Mean87.7
Minimum3.24
Maximum389
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.0 KiB
2024-07-19T15:44:46.319705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3.24
5-th percentile3.99
Q133.3
median45.3
Q353.2
95-th percentile339
Maximum389
Range386
Interquartile range (IQR)20

Descriptive statistics

Standard deviation109
Coefficient of variation (CV)1.24
Kurtosis0.698
Mean87.7
Median Absolute Deviation (MAD)10.1
Skewness1.52
Sum4.05 × 105
Variance1.19 × 104
MonotonicityNot monotonic
2024-07-19T15:44:46.462968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
46.4 19
 
0.4%
44 15
 
0.3%
44.5 14
 
0.3%
43.5 14
 
0.3%
43.8 14
 
0.3%
48.2 13
 
0.3%
46.7 13
 
0.3%
47.7 13
 
0.3%
44.9 13
 
0.3%
46.8 12
 
0.3%
Other values (2549) 4482
95.0%
(Missing) 94
 
2.0%
ValueCountFrequency (%)
3.24 1
< 0.1%
3.25 1
< 0.1%
3.344175824 1
< 0.1%
3.354626374 1
< 0.1%
3.365076923 1
< 0.1%
ValueCountFrequency (%)
389.16 1
< 0.1%
383.366 1
< 0.1%
383.0456 1
< 0.1%
381.294 1
< 0.1%
381.2414 1
< 0.1%

Crude_Protein
Real number (ℝ)

Distinct827
Distinct (%)22.7%
Missing1071
Missing (%)22.7%
Infinite0
Infinite (%)0.0%
Skewness0.0695
Mean12.2
Minimum1
Maximum20.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.0 KiB
2024-07-19T15:44:46.621932image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.4
Q110.2
median11.8
Q314.4
95-th percentile17
Maximum20.4
Range19.4
Interquartile range (IQR)4.16

Descriptive statistics

Standard deviation2.8
Coefficient of variation (CV)0.229
Kurtosis0.29
Mean12.2
Median Absolute Deviation (MAD)2.04
Skewness0.0695
Sum4.45 × 104
Variance7.82
MonotonicityNot monotonic
2024-07-19T15:44:46.786492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
11.3 44
 
0.9%
10.8 43
 
0.9%
9.2 34
 
0.7%
9.7 33
 
0.7%
10.7 33
 
0.7%
9.9 32
 
0.7%
10.9 31
 
0.7%
9.5 31
 
0.7%
10.2 31
 
0.7%
11.2 30
 
0.6%
Other values (817) 3303
70.0%
(Missing) 1071
 
22.7%
ValueCountFrequency (%)
1 5
0.1%
1.1 4
0.1%
1.2 4
0.1%
1.4 2
 
< 0.1%
1.5 4
0.1%
ValueCountFrequency (%)
20.4 1
< 0.1%
19.9 1
< 0.1%
19.8 1
< 0.1%
19.7 1
< 0.1%
19.4 1
< 0.1%

Starch
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct349
Distinct (%)38.8%
Missing3817
Missing (%)80.9%
Infinite0
Infinite (%)0.0%
Skewness-0.209
Mean72.7
Minimum65.6
Maximum78.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.0 KiB
2024-07-19T15:44:46.940039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum65.6
5-th percentile68.6
Q171.1
median72.8
Q374.5
95-th percentile76.4
Maximum78.8
Range13.2
Interquartile range (IQR)3.39

Descriptive statistics

Standard deviation2.42
Coefficient of variation (CV)0.0333
Kurtosis-0.351
Mean72.7
Median Absolute Deviation (MAD)1.7
Skewness-0.209
Sum6.54 × 104
Variance5.85
MonotonicityNot monotonic
2024-07-19T15:44:47.098386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
76.1 16
 
0.3%
75.5 14
 
0.3%
73.8 13
 
0.3%
74.5 13
 
0.3%
72.4 13
 
0.3%
72.5 12
 
0.3%
76.4 12
 
0.3%
73.6 11
 
0.2%
72.1 11
 
0.2%
75.2 11
 
0.2%
Other values (339) 773
 
16.4%
(Missing) 3817
80.9%
ValueCountFrequency (%)
65.6 2
< 0.1%
65.7 1
< 0.1%
66.4 1
< 0.1%
66.7 1
< 0.1%
66.8 1
< 0.1%
ValueCountFrequency (%)
78.8 4
0.1%
77.9 4
0.1%
77.8 4
0.1%
77.6 4
0.1%
77.15 1
 
< 0.1%

Crude_Oil
Real number (ℝ)

Distinct329
Distinct (%)36.2%
Missing3808
Missing (%)80.7%
Infinite0
Infinite (%)0.0%
Skewness-0.289
Mean43.5
Minimum34.7
Maximum50.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.0 KiB
2024-07-19T15:44:47.250127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum34.7
5-th percentile37.9
Q141.8
median43.9
Q345.3
95-th percentile48.6
Maximum50.9
Range16.2
Interquartile range (IQR)3.54

Descriptive statistics

Standard deviation3.03
Coefficient of variation (CV)0.0697
Kurtosis0.0522
Mean43.5
Median Absolute Deviation (MAD)1.8
Skewness-0.289
Sum3.95 × 104
Variance9.19
MonotonicityNot monotonic
2024-07-19T15:44:47.414413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
44.4 31
 
0.7%
45.3 28
 
0.6%
45.4 20
 
0.4%
44.3 19
 
0.4%
45.8 19
 
0.4%
45.1 17
 
0.4%
42.1 16
 
0.3%
44.2 16
 
0.3%
41.4 15
 
0.3%
45.2 14
 
0.3%
Other values (319) 713
 
15.1%
(Missing) 3808
80.7%
ValueCountFrequency (%)
34.7 1
< 0.1%
35.07 1
< 0.1%
35.24 1
< 0.1%
35.32 1
< 0.1%
35.65 1
< 0.1%
ValueCountFrequency (%)
50.9 1
 
< 0.1%
50.7 1
 
< 0.1%
50.4 1
 
< 0.1%
50.3 2
< 0.1%
50.2 3
0.1%

Interactions

2024-07-19T15:44:42.489616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:32.203963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:33.678681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:35.104247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:36.564313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:37.989445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:39.749582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:41.198458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:42.676217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:32.388042image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:33.856297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:35.297232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:36.739754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:38.180972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:39.951924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:41.381220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:42.858922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:32.562163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:34.011110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:35.473957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:36.899694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:38.351331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:40.119057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:41.544338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:43.029815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:32.748592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:34.188435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:35.663806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:37.083488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:38.541589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:40.305937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:41.719077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:43.198474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:32.928335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:34.357393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:35.834176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:37.241581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:39.032607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:40.526360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:41.875908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:43.373985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:33.117884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:34.533578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:36.026278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:37.425823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:39.221229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:40.721745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:42.030343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:43.490759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:33.298542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:34.731462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:36.198059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:37.620024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:39.404725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:40.899047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:42.204266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:43.615127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:33.475710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:34.914169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:36.373454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:37.794645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:39.566287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:41.079192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-19T15:44:42.369279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2024-07-19T15:44:47.964463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Experimental_YearPlot_IDYield_TotalDry_Matter_PropThousand_Kernel_MassCrude_ProteinStarchCrude_OilDateCrop
Experimental_Year1.0000.0010.045-0.007-0.125-0.1180.6680.1070.9860.105
Plot_ID0.0011.000-0.0030.0010.0130.0110.1040.0020.3700.024
Yield_Total0.045-0.0031.000-0.6670.642-0.2280.226-0.3310.5120.459
Dry_Matter_Prop-0.0070.001-0.6671.000-0.686NaNNaNNaN0.9751.000
Thousand_Kernel_Mass-0.1250.0130.642-0.6861.000-0.589-0.280-0.2950.6750.595
Crude_Protein-0.1180.011-0.228NaN-0.5891.000-0.475NaN0.5050.474
Starch0.6680.1040.226NaN-0.280-0.4751.000NaN0.5021.000
Crude_Oil0.1070.002-0.331NaN-0.295NaNNaN1.0000.4800.128
Date0.9860.3700.5120.9750.6750.5050.5020.4801.0000.985
Crop0.1050.0240.4591.0000.5950.4741.0000.1280.9851.000

Missing values

2024-07-19T15:44:43.872715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-19T15:44:44.155798image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-19T15:44:44.387041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Experimental_YearDatePlot_IDCropYield_TotalDry_Matter_PropThousand_Kernel_MassCrude_ProteinStarchCrude_Oil
020042004-08-09 00:00:00.00000001Winter wheat37.58648.010.9NaNNaN
120042004-08-09 00:00:00.00000002Winter wheat63.08648.514.2NaNNaN
220042004-08-09 00:00:00.00000003Winter wheat89.28646.814.7NaNNaN
320042004-08-09 00:00:00.00000004Winter wheat63.38640.514.1NaNNaN
420042004-08-09 00:00:00.00000005Winter wheat42.28642.815.1NaNNaN
520042004-08-09 00:00:00.00000006Winter wheat89.48649.215.3NaNNaN
620042004-08-09 00:00:00.00000007Winter wheat91.28648.514.1NaNNaN
720042004-08-09 00:00:00.00000008Winter wheat70.18643.512.1NaNNaN
820042004-08-09 00:00:00.00000009Winter wheat34.18642.715.6NaNNaN
920042004-08-09 00:00:00.000000010Winter wheat78.78646.316.1NaNNaN
Experimental_YearDatePlot_IDCropYield_TotalDry_Matter_PropThousand_Kernel_MassCrude_ProteinStarchCrude_Oil
470620232023-07-24 00:00:00.0000000231Winter wheat91.1589158635.2211.8NaNNaN
470720232023-07-24 00:00:00.0000000232Winter wheat98.7434118642.128.8NaNNaN
470820232023-07-24 00:00:00.0000000233Winter wheat87.0122748644.318.8NaNNaN
470920232023-07-24 00:00:00.0000000234Winter wheat95.6012278633.2212.3NaNNaN
471020232023-07-24 00:00:00.0000000235Winter wheat93.1259698635.2211.8NaNNaN
471120232023-07-24 00:00:00.0000000236Winter wheat94.8211248642.128.8NaNNaN
471220232023-07-24 00:00:00.0000000237Winter wheat90.1297168644.318.8NaNNaN
471320232023-07-24 00:00:00.0000000238Winter wheat75.1695748633.2212.3NaNNaN
471420232023-07-24 00:00:00.0000000239Winter wheat93.6879848635.2211.8NaNNaN
471520232023-07-24 00:00:00.0000000240Winter wheat97.5094968642.128.8NaNNaN